Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the over-detected false spans at span detection stage and the inaccurate and unstable prototypes at type classification stage remain to be challenging problems. In this paper, we propose a novel Type-Aware Decomposed framework, namely TadNER, to solve these problems. We first present a type-aware span filtering strategy to filter out false spans by removing those semantically far away from type names. We then present a type-aware contrastive learning strategy to construct more accurate and stable prototypes by jointly exploiting support samples and type names as references. Extensive experiments on various benchmarks prove that our proposed TadNER framework yields a new state-of-the-art performance.

翻译：尽管最近几个两阶段的原型网络成功完成了几个点名实体识别(NER)任务,但是,在探测阶段发现过度的假假跨线以及类型分类阶段的不准确和不稳定原型仍然是个具有挑战性的问题。在本文件中,我们提出了一个新型的软件解构框架,即TadNER,以解决上述问题。我们首先提出了一种类型识别过滤战略,通过清除远离类型名称的词义来过滤假跨线。然后,我们提出了一种类型识别对比式学习战略,通过联合利用支持样品和类型名称作为参考来构建更准确和稳定的原型。关于各种基准的广泛实验证明,我们提议的TadNER框架产生了新的最新性能。